Current status of antimicrobial peptides databases and computational tools for optimization Article Swipe
Madhulika Jha
,
Akash Nautiyal
,
Kumud Pant
,
Navin Kumar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36953/ecj.29252932
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36953/ecj.29252932
Antimicrobial resistance is projected as next pandemic with a propensity of around 10 million deaths per year by 2050. Alternatives of the antibiotic compounds are required to be explored on priority basis to treat viral, bacterial, and fungal infections. Antimicrobial peptides (AMPs) are emerging as a promising therapeutic alternative to combat serious microbial infections. Plenty of natural AMPs have been isolated and characterized from different sources posing a need of categorizing them in various databases. Bioinspired from natural peptides, several synthetic AMPs have been designed and tested against microbial pathogens. An ideal antimicrobial peptide needs to have specific physical and structural properties. To analyse structure, biological activity and toxicity of the AMPs, different computational tools have been developed which are available in the public domain accelerating the research and development of antimicrobial peptides. This review encompasses properties, structure and databases of antimicrobial peptide with a key focus on computational tools designed for prediction of structure, function and toxicity of the synthetic and natural peptides.
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Current status of antimicrobial peptides databases and computational tools for optimizationWork title
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2025-02-05Full publication date if available
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Madhulika Jha, Akash Nautiyal, Kumud Pant, Navin KumarList of authors in order
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| abstract_inverted_index.compounds | 23 |
| abstract_inverted_index.databases | 139 |
| abstract_inverted_index.developed | 117 |
| abstract_inverted_index.different | 64, 112 |
| abstract_inverted_index.microbial | 52, 88 |
| abstract_inverted_index.peptides, | 78 |
| abstract_inverted_index.peptides. | 132, 163 |
| abstract_inverted_index.projected | 3 |
| abstract_inverted_index.promising | 46 |
| abstract_inverted_index.structure | 137 |
| abstract_inverted_index.synthetic | 80, 160 |
| abstract_inverted_index.antibiotic | 22 |
| abstract_inverted_index.bacterial, | 35 |
| abstract_inverted_index.biological | 105 |
| abstract_inverted_index.databases. | 74 |
| abstract_inverted_index.pathogens. | 89 |
| abstract_inverted_index.prediction | 152 |
| abstract_inverted_index.propensity | 9 |
| abstract_inverted_index.resistance | 1 |
| abstract_inverted_index.structural | 100 |
| abstract_inverted_index.structure, | 104, 154 |
| abstract_inverted_index.Bioinspired | 75 |
| abstract_inverted_index.alternative | 48 |
| abstract_inverted_index.development | 129 |
| abstract_inverted_index.encompasses | 135 |
| abstract_inverted_index.infections. | 38, 53 |
| abstract_inverted_index.properties, | 136 |
| abstract_inverted_index.properties. | 101 |
| abstract_inverted_index.therapeutic | 47 |
| abstract_inverted_index.Alternatives | 19 |
| abstract_inverted_index.accelerating | 125 |
| abstract_inverted_index.categorizing | 70 |
| abstract_inverted_index.Antimicrobial | 0, 39 |
| abstract_inverted_index.antimicrobial | 92, 131, 141 |
| abstract_inverted_index.characterized | 62 |
| abstract_inverted_index.computational | 113, 148 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.82350906 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |